The goal of an auxiliary task in MTL is to learn useful shared representations for the main task (i.e., add a regularizing factor [1]}).
They are closely related to the main task, so adding them allows the model to learn beneficial representations.
However, finding an auxiliary task that helps improve the SS task is not trivial [2]}.
At first glance, tasks that seem different can use similar representations, and tasks that seem related can adjust different internal functions [3]}.
We still do not know which auxiliary tasks will help in practice for SS.
Finding an auxiliary task is largely based on the assumption that they should be related to the main task in some way.
We perceive, from Fig. REF , that spatial precision loss is generally produced on the edge of segmented objects.
So we use tasks related to the gradient or edge regions.
That is, we give more attention to the contours of the objects.
With this in mind, we propose to employ three types of contour-based auxiliary tasks to improve the boundary of segmented object and, therefore, the SS task.
We choose auxiliary tasks to reinforce and complement the information obtained from the edges of objects.
Thereby, we address the problem of spatial precision loss, generally reflected in the segmented objects' contours, cf. Fig. REF .
